DCT Based Digital Image Watermarking, De-

watermarking & Authentication

Pravin M. Pithiya

PG Student, Department of Electronics & Communication Engineering

SPCE, Visnagar, Gujarat, India

H.L.Desai

Assistant Prof. Department of Electronics & Communication Engineering

SPCE, Visnagar, Gujarat, India

Abstract— This paper introduces an algorithm of digital image watermarking based on Discrete Cosine Transform

(DCT). In this technique the embedding and extraction of the watermark is simple than other transform. In this

algorithm watermarking, de watermarking of the image is done and it also checks the authentication of the

watermarked image and de watermarked image. This paper also successfully explains digital image watermarking

based on discrete Cosine transform by analysing various performance parameters like PSNR,MSE,SNR and NC.

Keywords— Authentication, Digital watermarking, De watermarking, Discrete Wavelet Transform (DWT), Discrete

Cosines Transform (DCT,) human visual system (HVS), MSE(Mean Squared Error), NC(Normalised co relation

factor), PSNR(Peak Signal to Noise Ratio), SNR(Signal to Noise Ratio).

I. INTRODUCTION

The use of internet growing faster day to day and the need to display multimedia contents on the internet

become necessary. Intellectual property right; documents are not fast information but property. YouTube, face

book, Torrents, pirate bay such other video, audio, image, documents resource websites are now became water

and food for youngsters across the globe so it is necessary to protect the rights of authors. so digital protection is

necessary and in-evitable. There are many popular techniques for this such as Steganography, Digital signature,

Fingerprinting, cryptography and Digital watermarking but Digital watermarking is proved best out of them.

Digital watermarking is nothing but the technology in which there is embedding of various information in

digital content which we have to protect from illegal copying. This embedded information to protect the data is

embedded as watermark. Beyond the copyright protection, Digital watermarking is having some other

applications as Broadcast monitoring, Indexing, fingerprinting, owner identification, etc. Digital watermarks are

of different types as robust, fragile, semi fragile, visible and invisible. Application is depending upon these

watermarks classifications. There are some requirements of digital watermarks as integrity, robustness and

complexity.

In digital watermarking, a watermark is embedded into a cover image in such a way that the resulting

watermarked signal is robust to certain distortion caused by either standard data processing in a friendly

environment or malicious attacks in an unfriendly environment. This project presents a digital image

watermarking based on two dimensional discrete wavelet transform (DWT). Signal to noise ratio (SNR),

MSE(Mean Squared Error), NC(Normalised co relation factor), PSNR(Peak Signal to Noise Ratio) and

similarity ratio (SR) are computed to measure image quality for each transform..

According to Working Domain, the watermarking techniques can be divided into two types

a) Spatial Domain Watermarking Techniques

b) Frequency Domain Watermarking Techniques

In spatial domain techniques, the watermark embedding is done on image pixels while in frequency domain

watermarking techniques the embedding is done after taking image transforms. Generally frequency domain

methods are more robust than spatial domain techniques[1].

Spatial Domain Techniques

Spatial watermarking can also be applied using colour separation. In this way, the watermark appears in only

one of the colour bands. This renders the watermark visibly subtle such that it is difficult to detect under regular

viewing. However, the mark appears immediately when the colours are separated for printing. This renders the

document useless for the printer; the watermark can be removed from the colour band. This approach is used

commercially for journalists to inspect digital pictures from a photo-stock house before buying unmarked

versions[1].

(a) Least Significant Bit(LSB)

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The earliest work of digital image watermarking schemes embeds watermarks in the LSB of the pixels. Given

an image with pixels, and each pixel being represented by an 8-bit sequence, the watermarks are embedded in

the last (i.e., least significant) bit, of selected pixels of the image. This method is easy to implement and does not

generate serious distortion to the image; however, it is not very robust against attacks. For instance, an attacker

could simply randomize all LSBs, which effectively destroys the hidden information [1].

(b) SSM Modulation Based Technique

Spread-spectrum techniques are methods in which energy generated at one or more discrete frequencies is

deliberately spread or distributed in time. This is done for a variety of reasons, including the establishment of

secure communications, increasing resistance to natural interference and jamming, and to prevent detection.

When applied to the context of image watermarking, SSM based watermarking algorithms embed information

by linearly combining the host image with a small pseudo noise signal that is modulated by the embedded

watermark[1].

Frequency Domain Techniques

Compared to spatial-domain methods, frequency-domain methods are more widely applied. The aim is to embed

the watermarks in the spectral coefficients of the image. The most commonly used transforms are the Discrete

Cosine Transform (DCT), Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), The reason

for watermarking in the frequency domain is that the characteristics of the human visual system (HVS) are

better captured by the spectral coefficients. For example, the HVS is more sensitive to low-frequency

coefficients, and less sensitive to high-frequency coefficients. In other words, low-frequency coefficients are

perceptually significant, which means alterations to those components might cause distortion to the original

image. On the other hand, high-frequency coefficients are considered insignificant; thus, processing techniques,

such as compression, tend to remove high-frequency coefficients aggressively. To obtain a balance between

imperceptibility and robustness, most algorithms embed watermarks in the midrange frequencies[1][2].

(a)Discrete Cosine Transformation (DCT)

DCT like a Fourier Transform, it represents data in terms of frequency space rather than an amplitude space.

This is useful because that corresponds more to the way humans perceive light, so that the part that are not

perceived can be identified and thrown away. DCT based watermarking techniques are robust compared to

spatial domain techniques. Such algorithms are robust against simple image processing operations like low pass

filtering, brightness and contrast adjustment, blurring etc. However, they are difficult to implement and are

computationally more expensive. At the same time they are weak against geometric attacks like rotation,

scaling, cropping etc. DCT domain watermarking can be classified into Global DCT watermarking and Block

based DCT watermarking. Embedding in the perceptually significant portion of the image has its own

advantages because most compression schemes remove the perceptually insignificant portion of the

image[1][2][7].

(b)Discrete Wavelet Transformation (DWT)

The Discrete Wavelet Transform (DWT) is currently used in a wide variety of signal processing applications,

such as in audio and video compression, removal of noise in audio, and the simulation of wireless antenna

distribution. Wavelets have their energy concentrated in time and are well suited for the analysis of transient,

time-varying signals. Since most of the real life signals encountered are time varying in nature, the Wavelet

Transform suits many applications very well. One of the main challenges of the watermarking problem is to

achieve a better trade-off between robustness and perceptivity. Robustness can be achieved by increasing the

strength of the embedded watermark, but the visible distortion would be increased as well. However, DWT is

much preferred because it provides both a simultaneous spatial localization and a frequency spread of the

watermark within the host image. The basic idea of discrete wavelet transform in image process is to multi-

differentiated decompose the image into sub-image of different spatial domain and independent

frequencies[1][6] .

II. BASICS OF DCT

DCT separates images into parts of different frequencies where less important frequencies are discarded through

quantization and important frequencies are used to retrieve the image during decompression. Compared to other

input dependent transforms, DCT has many advantages:

(1) It has been implemented in single integrated circuit;

(2) It has the ability to pack most information in fewest coefficients;

(3) It minimizes the block like appearance called blocking artifact that results when boundaries between sub-

images become visible [15].

The 1D_DCT transformation is given by the following equation:

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for u = 0,1,2,…,N 1.

Similarly, the inverse transformation is defined as,

for x = 0,1,2,…,N 1.

In both equations 1 and 2 (u) is defined as ,

It is clear that for u=0,

the first transform coefficient is the average value of the sample sequence. In literature, this value is referred to

as the DC Coefficient.

The 2D_DCT transformation is given by the following equation:

This necessitates the extension of ideas presented in the last section to a two dimensional space. The 2-D DCT

is a direct extension of the 1-D case and is given by

Here , (u) and (v) are same as defined earlier part in equation 3.

All other transform coefficients are called the AC Coefficients.

Inverse DCT is also defined as ,

The discrete cosine transform (DCT) helps separate the image into parts (or spectral sub bands) of differing

importance (with respect to the image's visual quality). The DCT is similar to the discrete Fourier transform: it

transforms a signal or image from the spatial domain to the frequency domain. A discrete cosine transform

(DCT) expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at

different frequencies. DCTs are important to numerous applications in science and engineering, from lossy

compression of audio (e.g. MP3) and images (e.g. JPEG) (where small high-frequency components can be

discarded), to spectral for the numerical solution of partial differential equations. The use of cosine rather than

sine functions is critical in these applications: for compression, it turns out that cosine functions are much more

efficient (as described below, fewer are needed to approximate a typical signal), whereas for differential

equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-

related transform similar to the discrete Fourier transform (DFT), but using only real numbers [10].

III. PROPOSED METHOD OF WATERMARK EMBEDDING & EXTRACTION PROCESS:

3.1 Watermark Embedding using DCT:

The Procedure of watermark embedding is shown in fig.1.

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Figure 1 Flowchart of Watermark Embedding Process

Algorithm for watermark embedding :

The Watermark embedding steps using this technique are following:

1. Read colour host image.

2. Convert RGB to YCbCr components.

3. Apply DCT.

4. Embed the watermark components in to the frequency subcomponents .

5. Apply IDCT.

6. Convert YCbCr to RGB.

7. Get watermarked image

8. Check Authentication.

3.2 Watermark Extraction using DCT:

The Procedure of watermark extracting is shown in fig.2.

Figure 2 Flowchart of Watermark Extraction Process

Algorithm for watermark extraction :

The Watermark extraction steps using this technique are following:

1. Read Watermarked image.

2. Convert RGB to YCbCr components.

3. Apply DCT.

4. Extract the watermark components from frequency subcomponents .

5. Convert YCbCr to RGB.

6. Get watermark image

7. Check Authentication.

8. Calculate for MSE,PSNR,SNR&NC.

IV. PERFORMANCE EVOLUTION

To evaluate the performance of the proposed method, we have to check the performance parameters. In this

experiment the host image is LENA of size 512*512 and the watermark image is a symbol of size 256*256

which is shown in figure 4 respectively. In our proposed algorithm, we have set gain factor =0.5.This algorithm

has been implemented using MATLAB 10a.

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Imperceptibility: Imperceptibility means that the perceived quality of the host image should not be distorted by

the presence of the watermark. To measure the quality of a watermarked image , the peak signal to noise ratio

(PSNR) is used. The Visual quality of a watermarked image is evaluated by the peak signal-to-noise ratio

(PSNR).

It is defined as:

Where, MSE=Mean Squared Error between Original and distorted image. which is defined

Robustness: Robustness is a measure of the immunity of the watermark against attempts to remove or degrade it,

internationally or unintentionally, by different types of digital signal processing attacks. We measured the

similarity between the original watermark and the watermark extracted from the attacked image using the

Normalized correlation factor given below Eq.

Where N*N is the size of watermark, W ( i j ) and W’ ( i j ) are the watermark and recovered watermark

images respectively[15].

V. SIMULATION RESULTS:

This section described results of proposed method for watermarking, de watermarking and authentication.

Figure 3: Original Image

Figure 4: Watermark Image

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Figure 5: Watermarked Image and Difference image

Figure 6 De- watermarked Image and Difference image

Table 1: Observation for authentication between original image and watermarked image

Parameter value

Window size 5

Random point in image [ 417 464 ]

Original Image intensity 207.6

Watermarked Image intensity 227.12

Result: Watermarked Image is authenticated.

Table 2: Observation for authentication between original image and de watermarked image

Parameter value

Window size 5

Random point in image [ 417 464 ]

Original Image intensity 207.6

De Watermarked Image intensity 227.2

Result: De Watermarked Image is authenticated.

Table 3: Observation of Performance Parameters for RGB of image

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Parameters Red Green Blue

MSE 12.5779 13.9554 13.9678

PSNR 47.9605 47.5092 47.5053

SNR 89.1276 84.6104 84.5127

NC 0.99962 0.99894 0.99894

VI. CONCLUSION

There are many types of algorithms for digital image watermarking. Each type of algorithms has its own

advantages and disadvantages. No method has perfect solution for digital watermarking. Each type has

robustness to some type of attacks but is less efficient to some other types of attacks. each type of digital

watermarking depends on the nature of application and requirements. In this paper we presented a new method

of embedding watermark into colour image. The RGB image is converted to YCbCr and watermarked by using

discrete cosine transform (DCT). The luminance component Y of image is considered for embedding watermark.

The performance of the proposed method can be evaluated by PSNR,SNR,MSE and NC for RED,BLUE and

GREEN. Existing techniques have worked on the gray scale of image, we have taken results for RED,BLUE

and GREEN separately. Proposed technique results have shown that technique presented in this paper is very

effective for watermarking and de watermarking authentication and also support more security and exact

correlation between original watermark and extracted watermark.

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